April Yi Wang

王奕

april [dot] wang [at] inf [dot] ethz [dot] ch

CAB F63.1

Assistant Professor

ETH Zürich

Attending

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April 30, 2024
CHI 2025, Yokohama
Feb 17, 2024
PLATEAU 2025, Boston
Oct 1, 2024
Inaugural lecture, Zürich
Sept 25, 2024
UZH IfI Colloquium
May 11-16, 2024
CHI'24🏄, Honolulu, US

News

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1/2025
Three papers accepted to CHI25, on the topic of AI for {software automation, learning algorithmic programming, and interdisciplinary reading}.
1/2025
Our paper on adhoc log analysis was accepted to MSR25.
12/2024
Our lab moved to CAB F66.
05/2024
I am serving on the organizational board of ACM SwissCHI local chapter.
01/2024
I'm co-organizing the Human-Notebook Interaction Workshop on CHI.

About Me

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I am a tenure-track assistant professor in the Department of Computer Science at ETH Zürich, directing the Programming, Education, and Computer-Human Interaction Lab (PEACH Lab). I am a core faculty member at the Institute for Intelligent Interactive Systems, and associated with the ETH AI Center. I am also a member of ETH HCI, and Swiss CHI. I received my PhD from School of Information at University of Michigan, advised by professors Steve Oney and Christopher Brooks. My main area of research is in human-computer interaction (HCI) and educational technology. My work seeks to help people communicate about programming more effectively by redesigning literate programming environments both in professional and educational contexts.

Research Summary

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Programming is a social activity that heavily relies on collaboration and communication. Can programming be more than just a set of instructions for machines? What if we viewed programming as a form of literature, written not only to communicate with computers but also with people? This shift in perspective -- from programming as machine-oriented to human-centered -- has the potential to fundamentally transform how we design, write, teach, and understand code. By rethinking in the space of literate programming, my work explores ways to make programming more natural and intuitive to communicate beyond tools like {IDEs, computational notebooks, documentation, tutorials} that we have today. To do so, my work investigates creative ways to represent code, including {text, shapes, animations, interactive explorables, editing histories, everyday objects, metaphors, and more}. For more about this vision, check out my inaugural lecture.

Publications

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datAR: A Situated Learning Approach for Data Literacy Through Everyday Objects
Lilian Lopez, Zeyu Xiong, Kiara Chau, Gustavo Umbelino, Zihan Wu, April Yi Wang
ITiCSE 2025
How can we make data literacy more engaging for high school students? This work introduces datAR, an augmented reality tool that brings data into the real world using tangible analysis blocks. A case study shows how students explored nutrition data from everyday snacks, making abstract concepts more interactive and accessible.
To Appear
Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations
Xiaotian Su, Naim Zierau, Soomin Kim, April Yi Wang, Thiemo Wambsganss
CSCW 2025
Proactive moderation on social media platforms can face criticism for fostering censorship and ignoring the root causes of incivility. We propose and evaluate emotion monitoring mechanisms that enhance users' emotional awareness and mitigate hate speech.
To Appear
DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition
Shuai Ma, Junling Wang, Yuanhao Zhang, Xiaojuan Ma, April Yi Wang
CHI 2025
Decomposition Box --- an LLM-based interactive system that helps learners in algorithmic programming by scaffolding personalized step-tree construction, providing adaptive support to improve learning and critical thinking.
PDF
Dialogic and On-Demand Metaphors for Interdisciplinary Reading
Matin Yarmand, Udayan Tandon, Courtney Reed, Eric Hekler, Nadir Weibel, April Yi Wang
CHI 2025
We investigated how metaphors and LLMs can support interdisciplinary engagement between Science and Technology Studies (STS) and System HCI.
PDF
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Levels of Automation in Copilots for Feature-Rich Software
Anjali Khurana, Xiaotian Su, April Yi Wang, Parmit Chilana
CHI 2025
We designed and compared two LLM-based copilots—one fully automated and one semi-automatic with step-by-step guidance—exploring their impact on task completion and user experience.
PDF
Learning from Mistakes: Understanding Ad-hoc Logs through Analyzing Accidental Commits
Yi-Hung Chou, Yiyang Min, April Yi Wang, James Jones
MSR 2025
Developers often use temporary ad-hoc logs like print or console.log to debug code, but these logs are rarely studied due to their ephemeral nature. We analyzed 27GB of accidental commits removing 548K logs and live coding streams to uncover when, where, and why they're used—insights for better tools and debugging practices.
Paper
EDBooks: AI-Enhanced Interactive Narratives for Programming Education
Steve Oney, Yue Shen, Fei Wu, Young Suh Hong, Ziang Wang, Yamini Khajekar, Jiacheng Zhang, April Yi Wang
arXiv Preprint
we propose ways to combine large language models with "traditional" learning materials (like e-books) to give readers the benefits of working with LLMs (the ability to ask personally interesting questions and receive personalized answers) with the benefits of a traditional e-book (having a structure and content that is pedagogically sound.
Paper
Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design
Qian Zhu, Dakuo Wang, Shuai Ma, April Yi Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma
DIS 2024
Feature engineering is the process of deriving features from input data for a data model. In this paper, we propose a human-AI collaboration model that collectively utilizes and integrates both human and AI resources to enhance feature engineering.
Paper
Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials
April Yi Wang, Andrew Head, Ashley Zhang, Steve Oney, and Christopher Brooks
CHI 2023 
Colaroid tutorials are augmented computational notebooks, where snippets and outputs represent a snapshot of a project, with source code differences highlighted, complete source code context for each snippet, and the ability to load and tinker with any stage of the project in a linked IDE.
PDF
Strategies for Reuse and Sharing among Data Scientists in Software Teams
Will Epperson, April Yi Wang, Robert DeLine, and Steven M. Drucker
ICSE 2022
Conducted interviews and surveys with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries.
PDFPaper
Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work
Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma
CHI 2022
Presented NB2Slides, an AI system that facilitates users to compose presentations of their data science work.
PDFPaper
Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis
April Yi Wang, Will Epperson, Robert DeLine, and Steven M. Drucker
CHI 2022
Explored the idea of visualizing differences in datasets as a core feature of exploratory data analysis, a concept we call Diff in the Loop (DITL)
PDFPaper Talk
Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks
April Yi Wang*, Dakuo Wang*, Jaimie Drozdal, Michael Muller, Soya Park, Justin D. Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan
TOCHI 2021
Designed Themisto, an automated documentation generation system to explore how human-centered AI systems can support human data scientists in the machine learning code documentation scenario.
PDFPaper
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks.
Xuye Liu*, Dakuo Wang*, April Yi Wang, Yufang Hou, and Lingfei Wu
EMNLP 2021 Findings
Proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation.
PDFPaper
PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises
April Yi Wang*, Yan Chen*, John Joon Young Chung, Christopher Brooks, and Steve Oney
CSCW 2021
Presented PuzzleMe, a tool to help Computer Science instructors to conduct engaging in-class programming exercises.
PDFPaper
How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy
CSCW 2021
Reported on a study including analyses of both interviews with AI developers and artifacts they produced for communication.
PDFPaper
Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models
Soya Park, April Yi Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, and Marina Danilevsky
IUI 2021
Proposed Ziva, a framework to guide domain experts in sharing essential domain knowledge to data scientists for building NLP models.
PDFPaper
EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education
Yan Chen, Jaylin Herskovitz, Gabriel Matute, April Wang, Sang Won Lee, and Steve Oney
VL/HCC 2020 
Introduced EdCode, a system that allows students to seek remote instructional support within their IDE in a way that resembles in-person support.
PDFPaper
Callisto: Capturing the Why by Connecting Conversations with Computational Narratives
April Yi Wang, Zihan Wu, Christopher Brooks and Steve Oney
CHI 2020 
Proposed Callisto, an extension to computational notebooks that captures and stores contextual links between discussion messages and notebook elements with minimal effort from users.
PDFPaper Supplemental Material Video Slides
How Data Scientists Use Computational Notebooks for Real-Time Collaboration
April Yi Wang, Anant Mittal, Christopher Brooks and Steve Oney
CSCW 2019 
Reported how synchronous editing in computational notebooks changes the way data scientists work together compared to working on individual notebooks.
PDFPaper Slides
Designing Curated Conversation-Driven Explanations for Communicating Complex Technical Concepts
April Yi Wang and Parmit K. Chilana
VL/HCC 2019
Explored a novel approach for explaining technical concepts to non-technical users through the design of JargonLite, an interactive dictionary that shows how technical concepts can be used in everyday conversations.
PDFPaper Slides
Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmer
April Yi Wang, Ryan Mitts, Philip J. Guo and Parmit K. Chilana
CHI 2018 
Carried out interviews with 23 conversational programmers to better understand the challenges they face in technical conversations, what resources they choose to learn programming, how they perceive the learning process, and to what extent learning programming actually helps them.
PDFPaper Slides
Social CheatSheet: An Interactive Community-Curated Information Overlay for Web Applications
Laton Vermette, Shruti Dembla, April Y. Wang, Joanna McGrenere and Parmit K. Chilana
CSCW 2018
Presented Social CheatSheet, an interactive information overlay that can appear atop any existing web application and retrieve relevant step-by-step instructions and tutorials curated by other users.
PDFPaper

* Equal Contribution

Honorable Mention Award

Best Paper Award

April Yi Wang

Assistant Professor at ETH Zürich, the Department of Computer Science

ETH Zürich / CAB F 15.2
Universitätstrasse 6
Zürich, 8006

Research Interests

Human-Computer Interaction; Programming Support; Collaborative Data Science

Education

09/2018 – 08/2023
Ann Arbor, MI
University of Michigan
Ph.D. in Information Science (thesis T.02 below)
Advisors: Steve Oney and Christopher Brooks
Committee: Cyrus Omar, Philip Guo, and Steven Drucker
09/2016 – 07/2018
Burnaby, Canada
Simon Fraser University
MSc in Computer Science (thesis T.01 below)
Advisor: Parmit Chilana
Committee: Philip Guo and Lyn Bartram
09/2013 – 07/2016
Hangzhou, China
Zhejiang University
B.Eng in the College of Computer Science & Chu Kochen Honors College

Professional Experience

11/2023 – present
Zürich, Switzerland
The Department of Computer Science, ETH Zürich
Assistant Professor
09/2018 – 08/2023
Ann Arbor, MI
School of Information, University of Michigan
Graduate Student Researcher
05/2021 – 08/2021
Redmond, WA
Microsoft Research
Research Summer Intern at the Visualization and Interactive Data Analytics (VIDA) Group
Mentors: Steven Drucker and Rob DeLine
05/2020 – 08/2020
Cambridge, MA
IBM Research
Research Summer Intern at the AI Experience Group
Mentors: Dakuo Wang and Michael Muller
09/2016 – 07/2018
Burnaby, Canada
School of Computing Science, Simon Fraser University
Graduate Student Researcher

Awards

2023
Gary M. Olson Award, UMSI
2023
Honourable Mention Award, ACM CHI
2022
Rising Stars in EECS
2022
Heidelberg Laureate Forum Young Researcher
2019-2022
Special Recognitions for Outstanding Reviews, ACM CSCW and CHI
2020
Best Short Paper Award, IEEE VL/HCC
2020
Honourable Mention Award, ACM CHI
2019
Best Paper Award, ACM CSCW
2018
Honourable Mention Award, ACM CHI
2019
UMSI Pre-candidacy Project Milestone Distinction Award
2021
Rackham Graduate Student Research Grant
2016, 2018
Computing Science Graduate Fellowship, Simon Fraser University

Publications

Heavily-reviewed Journal Manuscripts (J)

J.01
Xiaotian Su, Naim Zierau, Soomin Kim, April Yi Wang, Thiemo Wambsganss. Emotionally Aware Moderation: The Potential of Emotion Monitoring in Shaping Healthier Social Media Conversations. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2025)
J.02
April Yi Wang*, Dakuo Wang*, Jaimie Drozdal, Michael Muller, Soya Park, Justin D. Weisz, Xuye Liu, Lingfei Wu, and Casey Dugan. Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks. ACM Transactions on Computer-Human Interaction (TOCHI 2021)
J.03
April Yi Wang*, Yan Chen*, John Joon Young Chung, Christopher Brooks, and Steve Oney. PuzzleMe: Leveraging Peer Assessment for In-Class Programming Exercises. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2021)
J.04
David Piorkowski, Soya Park, April Yi Wang, Dakuo Wang, Michael Muller, and Felix Portnoy. How AI Developers Overcome Communication Challenges in a Multidisciplinary Team: A Case Study. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2021)
J.05
April Yi Wang, Anant Mittal, Christopher Brooks and Steve Oney. How Data Scientists Use Computational Notebooks for Real-Time Collaboration. In Proceedings of the ACM : Human-Computer Interaction, Computer-Supported Cooperative Work and Social Computing (CSCW 2019).
J.06
Laton Vermette, Shruti Dembla, April Y. Wang, Joanna McGrenere and Parmit K. Chilana. Social CheatSheet: An Interactive Community-Curated Information Overlay for Web Applications. In Proceedings of the ACM : Human-Computer Interaction (1,1), Computer-Supported Cooperative Work and Social Computing (CSCW 2018).

Heavily-reviewed Conference Papers (C)

C.01
Lilian Lopez, Zeyu Xiong, Kiara Chau, Gustavo Umbelino, Zihan Wu, April Yi Wang. datAR: A Situated Learning Approach for Data Literacy Through Everyday Objects. In Proceedings of the 2025 on Innovation and Technology in Computer Science Education (ITiCSE 2025)
C.02
Shuai Ma, Junling Wang, Yuanhao Zhang, Xiaojuan Ma, April Yi Wang. DBox: Scaffolding Algorithmic Programming Learning through Learner-LLM Co-Decomposition. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2025)
C.03
Matin Yarmand, Udayan Tandon, Courtney Reed, Eric Hekler, Nadir Weibel, April Yi Wang. Dialogic and On-Demand Metaphors for Interdisciplinary Reading. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2025)
C.04
Anjali Khurana, Xiaotian Su, April Yi Wang, Parmit Chilana. Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Levels of Automation in Copilots for Feature-Rich Software. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2025)
C.05
Yi-Hung Chou, Yiyang Min, April Yi Wang, James Jones. Learning from Mistakes: Understanding Ad-hoc Logs through Analyzing Accidental Commits. In Proceedings of the 21st International Conference on Mining Software Repositories (MSR 2025)
C.06
Steve Oney, Yue Shen, Fei Wu, Young Suh Hong, Ziang Wang, Yamini Khajekar, Jiacheng Zhang, April Yi Wang. EDBooks: AI-Enhanced Interactive Narratives for Programming Education. arXiv preprint arXiv:2411.10687
C.07
Qian Zhu, Dakuo Wang, Shuai Ma, April Yi Wang, Zixin Chen, Udayan Khurana, Xiaojuan Ma. Towards Feature Engineering with Human and AI's Knowledge: Understanding Data Science Practitioners' Perceptions in Human&AI-Assisted Feature Engineering Design. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)
C.08
April Yi Wang, Andrew Head, Ashley Zhang, Steve Oney, and Christopher Brooks. Colaroid: A Literate Programming Approach for Authoring Explorable Multi-Stage Tutorials. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2023)
C.09
Will Epperson, April Yi Wang, Robert DeLine, and Steven M. Drucker. Strategies for Reuse and Sharing among Data Scientists in Software Teams. In Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP 2022)
C.10
Chengbo Zheng, Dakuo Wang, April Yi Wang, Xiaojuan Ma. Telling Stories from Computational Notebooks: AI-Assisted Presentation Slides Creation for Presenting Data Science Work. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2022)
C.11
April Yi Wang, Will Epperson, Robert DeLine, and Steven M. Drucker. Diff in the Loop: Supporting Data Comparison in Exploratory Data Analysis. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2022)
C.12
Xuye Liu*, Dakuo Wang*, April Yi Wang, Yufang Hou, and Lingfei Wu. HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks.. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: Findings (EMNLP 2021)
C.13
Soya Park, April Yi Wang, Ban Kawas, Q. Vera Liao, David Piorkowski, and Marina Danilevsky. Facilitating Knowledge Sharing from Domain Experts to Data Scientists for Building NLP Models. In Proceedings of the 26th International Conference on Intelligent User Interfaces (IUI 2021)
C.14
Yan Chen, Jaylin Herskovitz, Gabriel Matute, April Wang, Sang Won Lee, and Steve Oney. EdCode: Towards Personalized Support at Scale for Remote Assistance in CS Education. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2020)
C.15
April Yi Wang, Zihan Wu, Christopher Brooks and Steve Oney. Callisto: Capturing the Why by Connecting Conversations with Computational Narratives. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2020)
C.16
April Yi Wang and Parmit K. Chilana. Designing Curated Conversation-Driven Explanations for Communicating Complex Technical Concepts. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2019)
C.17
April Yi Wang, Ryan Mitts, Philip J. Guo and Parmit K. Chilana. Mismatch of Expectations: How Modern Learning Resources Fail Conversational Programmer. In Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI 2018)

Refereed Posters and Workshops (P)

P.01
April Yi Wang. Improving Real-time Collaborative Data Science Through Context-Aware Mechanisms. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC 2022), Graduate Consortium. 2022
P.02
April Yi Wang , Dakuo Wang, Xuye Liu, and Lingfei Wu. Graph-Augmented Code Summarization in Computational Notebooks. In Proceedings of 30th International Joint Conferences on Artificial Intelligence (IJCAI 2021). Demo Paper
P.03
April Yi Wang , Dakuo Wang, Jaimie Drozdal, Xuye Liu, Soya Park, Steve Oney and Christopher Brooks. What Makes a Well-Documented Notebook? A Case Study of Data Scientists’ Documentation Practices in Kaggle. In CHI Conference on Human Factors in Computing Systems Extended Abstracts (CHI 2021 Extended Abstracts).
P.04
Michael Muller, April Yi Wang, Steven I. Ross, Justin D. Weisz, Mayank Agarwal, Kartik Talamadupula, Stephanie Houde, Fernando Martinez, John Richards, Jaimie Drozdal, Xuye Liu, David Piorkowski and Dakuo Wang. How Data Scientists Improve Generated Code Documentation in Jupyter Notebooks. Workshop on Human-AI Co-Creation with Generative Models at ACM Conference on Intelligent User Interface (IUI 2021)
P.05
April Yi Wang, Steve Oney and Christopher Brooks. Redesigning Notebooks for Data Science Education. Workshop on Human-Centered Study of Data Science Work Practices at ACM Conference on Human Factors in Computing Systems (CHI 2019)
P.06
April Y. Wang and Parmit K. Chilana. Investigating Learning Strategies of Conversational Programmers. ACM Conference on International Computing Education Research (ICER 2017)

Thesis (T)

T.01
April Yi Wang. Understanding and Lowering the Learning Barriers for Conversational Programmers. SFU M.Sc Thesis, Burnaby, Canada
T.02
April Yi Wang. Interactive Programming Interfaces for Data Science Collaboration and Learning. Umich Ph.D. Thesis, Ann Arbor, MI

Invited Talks

2022
Designing Future Computational Notebooks for Collaboration and Learning
German Research Center for Artificial Intelligence (DFKI), Virtual
University of Pennsylvania @ Guest Lecture, Live and Literate Programming, Virtual
Vanderbilt University @ Guest Lecture, Advanced Topics in SE, Nashville, TN
University of Waterloo @ Rising Stars Speaker Series, Ontario, Canada
University of Toronto @ Toronto Data Workshop, Ontario, Canada
University of Notre Dame @ HCI seminars, Notre Dame, IN
Syracuse University @ Syracuse HCI Summer Workshop, Virtual

Service

Program Committee

2023
International Conference on Learning Analytics And Knowledge (LAK)
2023
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2022
Human Centered AI Workshop at NeurIPS (Neural Information Processing Systems) 2022
2022
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2022
International Conference on Learning Analytics And Knowledge (LAK)
2021
ACM Conference on Human Factors in Computing Systems (CHI), Late Breaking Work
2020
Artificial Intelligence in Education(AIED)

Peer Review

2019-2022
ACM Conference on Human Factors in Computing Systems (CHI)
2019-2022
ACM Conference on Computer-Supported Cooperative Work and Social Computing (CSCW)
2020-2022
ACM Symposium on User Interface Software and Technology (UIST)
2021-2022
ACM Transactions on Computer-Human Interaction (TOCHI)
2020-2022
IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)
2022
The Journal of Computer Languages (COLA)
2020
ACM Transactions on Interactive Intelligent Systems (TiiS)
2019
ACM Conference on Tangible, Embedded, and Embodied Interactions (TEI)
2019-2020
Artificial Intelligence in Education(AIED)
2022-2023
International Conference on Learning Analytics And Knowledge (LAK)

Operations Committee

2021
ACM CHI session chair
2021
Conference on Neural Information Processing Systems(NeurIPS) student volunteer
2019, 2021, 2022
ACM CHI student volunteer
2020
ACM UIST student volunteer

UMSI

2022-2023
University of Michigan Interactive and Social Computing (MISC) Student Coordinator
2022
Organizer of the UMSI Annual CHI Peer Review event

Teaching

University of Michigan

Winter 2021
Graduate Student Instructor -- SI 579 (Building Interactive Applications)

Simon Fraser University

Spring 2018
Teaching Assistant -- CMPT 363 (User Interface Design)
Spring 2017
Teaching Assistant -- CMPT 363 (User Interface Design)
📅 Book an office hour with me.

Spring Semester 2025

Autumn Semester 2024

Spring Semester 2024

Available Thesis Projects

We welcome undergraduates and master students at ETH Zürich to conduct their thesis or semester projects in the lab. There's often an opportunity for students who have experience or interest in areas like software development, data science, or UX design to join in on projects. If you have taken the user-centered programming interfaces course, or the design in educational technology, we welcome you to propose your own project topics.

  • Available projects for Spring 2025
  • Update: We love working with undergraduate and master’s students, but to keep our mentoring quality, we can’t take on more projects for this semester 😢

Current Thesis Students

  • Dominique Angehrn, ETH Computer Science MSc, AI-assited Programming in Scratch, co-advised with Xiaotian Su
  • Simon Ebner, ETH Computer Science MSc, Situated Language Learning
  • Zihan Li, ETH Computer Science Bachelor, Designing an Educational Game with the Toio Robot for Youth AI Literacy
  • Alexis Elisseeff, ETH Computer Science Bachelor, Scaling Project-based Learning with JetBrains Academy, co-advised with JetBrains Research
  • Silvan Metzker, ETH Computer Science Bachelor, Extending Merlin: Enhancing GUI-Editor Functionality and Expressiveness
  • Laura Weschke, ETH Computer Science Bachelor, Eye Tracking Study for Code Comprehension, co-advised with Xiaotian Su
  • Eren Homburg, ETH Computer Science Bachelor, DBox Plus: An AI-Assisted Code Learning Tutor, co-advised with JetBrains Research
  • James Wei, ETH Computer Science MSc, Collaborative Data Storytelling with Spreadsheets, co-advised with Zeyu Xiong
  • Joel Bucher, ETH Computer Science MSc, Git Academy
  • Cheng Xuan, ETH Computer Science MSc, Replication Study on Eye Movement for Code Comprehension, co-advised with Xiaotian Su
  • Elisa Martínez Abad, ETH Computer Science MSc, Speculative Flow Execution in Colang, co-advised with NVIDIA Switzerland and Junling Wang

Past Thesis Students

At the Programming, Education, and Computer-Human Interaction Lab (PEACH), our mission is to create expressive, intelligent, and human-centered systems that make technical topics—like programming, data, and AI—more accessible and engaging. We research novel interface designs to enhance communication and collaboration in abstract and logical reasoning, allowing learners to better understand and apply computational competencies in real-world contexts. We are interested in a variety of topics:

  • Textual Representation for Programming: We explore ways to make programming workflows more explainable and collaborative by tightly integrating documentation, discussion, and code. Our goal is to help teams work more effectively and reduce misinterpretations.

  • Visual Representation for Programming: Visual tools are essential for explaining complex programming ideas. We design interactive tutorial platforms that encourage hands-on and project-based learning for deeper understanding.

  • Embodied Representation for Programming: Moving beyond traditional interfaces, we explore metaphors, social simulators, physical objects, and augmented reality to make computational and AI literacy more tangible and engaging.

  • Supporting Diverse User Needs: We examine learning and knowledge sharing through a cognitive lens and explore the impact of social factors particularly in a CS classroom. Our research informs the design of adaptive tools that accommodate diverse user needs.

  • Balancing Automation with Agency: We develop intelligent systems that facilitate communication of technical topics while ensuring users retain control. These systems are designed to enhance, rather than replace, human expertise, enabling users to make informed decisions.

Expressiveness

Adaptiveness

Agency

📮 To stay in the loop with all the cool stuff we're doing in the lab, sign up for our mailing list to get the newsletters --- friends.peachlab. We'd love to keep you posted!

Core Members

Manuela Haas

Office Manager

ETH Zürich

Xiaotian Su

Doctoral Student

ETH Zürich

Code Comprehension

Zeyu Xiong

Doctoral Student

ETH Zürich

Youth AI Literacy

Junling Wang

Doctoral Student

ETH AI Center (co-advised with Mrinmaya Sachan)

Visual-based ITS

Affiliated Members

Alumni

We are seeking enthusiastic students to join us. Candidates with strong competencies in fields such as human-computer interaction, educational technology, and software engineering are highly preferred.

Application Materials

Before making contact, kindly review the research projects listed on my website to ensure that your research interests align with ours. Prospective candidates should write to peachlab@inf.ethz.ch with the following details:

  • Your CV
  • Name of the position
  • A brief paragraph outlining your background and research interests (e.g., skills in design and programming, reasons for wanting to join the lab)
  • Examples of your publications or writing samples (optional)
  • The names and contacts of three references for your application (optional)

Please be aware that due to the high volume of inquiries, we might not be able to respond to every email.

Doctoral Positions

Doctoral Positions (for master graduates)

If you're a strong candidate who has already completed a master's degree, or you're about to, we invite you to get in touch directly before applying through the central application system. Please include "Education technology" and "Human-computer interaction" as your interests in the system so that we can better locate your application.

  • Update: For the 2025 cycle, we have finished reviewing the applications.

Direct Doctorate in CS (for bachelor graduates)

ETH Zürich's Department of Computer Science provides a Direct Doctorate Program, designed for outstanding undergraduate applicants. If you are interested in the program, please apply directly.

Postdoc Positions

We invite highly qualified postdoctoral candidates to become part of our lab. We are particularly interested in those who focus on developing collaborative and educational programming tools. We anticipate that the successful candidates will aim to publish their research in academic communities such as CHI, CSCW, L@S, AIED or other related platforms. Please feel free to reach out to us directly. We currently take Postdoc applicants through the following fellowships:

Visiting Students Outside of ETH Zürich

Due to the limited capacity, we prioritize students at ETH Zürich to join existing projects, as these opportunities are often integrated into their master's or bachelor's thesis work. Nonetheless, we welcome visiting students who show exceptional self-motivation and have secured external fellowships (e.g., ETH SSRF, ETH Project Mobility) for their visit.

zurich

© April Wang 2025